1. Field of the Invention
The present invention discloses a method of locating a license plate of a moving vehicle, and more particularly, to a method of locating license plate of a moving vehicle by edge detection, binarization, morphological operations, and calculating edge densities.
2. Description of the Prior Art
In recent years, with the aid of improved technologies and popularities of computers, a monitoring system for monitoring roads is not merely required to acquire conventional recording functions, but more various applications related to computer networks and image processing, and as a result, information embedded in recorded monitoring images can be retrieved for references in a convenient, simple, popular manner. Besides, with the increased amount of vehicles, various problems about traffic security and related law enforcements also arise, such as parking area management, escapees of freeway charges, tracking of vehicles breaking traffic security codes, searching of stolen vehicles, monitoring of moving vehicles at roads. Thereby, researches on intelligent transportation systems (ITS) are prompted.
Recognition of a license plate of a moving vehicle is a primary application for the ITS. However, before performing the recognition, locating the license plate becomes a critical issue, i.e., if an outcome of locating the license plate is unclear, recognition of the license plate may fail under a great chance. In the recognition of the license plate, a license plate template is first retrieved by segmenting visual objects, and the image recognition technologies are used for recognizing the numbers on the license plate. However, there are large amounts of researches on recognizing the numbers on the license plate, whereas there are few researches on segmenting (or locating) the license plate template of a moving vehicle. Therefore, moving/visual object segmentation becomes a required prior core technology in developing ITS, on computer visual applications including detection, recognition, counting, and tracking of a moving vehicle.
For license plate locating, characteristics of the license plate, such as edge, contrast, and colors, are directly used for searching and locating the license plate on an image in certain methods. With respect to the edge, the location of the license plate on the image is assumed to acquire most-significant variation, therefore, edge algorithms may be used for locating edges of the license plate, for example, locating the location of the license plate by using a mask or morphological operations. In “A fast license plate extraction method on complex background”, which is edited by H. L. Bai, J. M. Zhu and C. P. Liu, and is published in Proc. IEEE Intelligent Transportation Systems, vol. 2, pp. 985-987, on October 2003, a freeway charging system is proposed. Since the proposed freeway charging system is required to rapidly and correctly recognizing a license plate, the license plate is located by using vertical edge detection, an edge density map, binarization, and dilation. In “A robust license-plate extraction method under complex image conditions”, which is edited by Sunghoon Kim, Daechul Kim, Younbok Ryu, Gyeonghwan Kim, and is published in IEEE International Conference on Pattern Recognition, vol. 3, pp. 216-219, on 2002, a robust license plate locating method is proposed. The proposed method includes two primary steps. In the first step, the location of the license plate is searched according to gradients on an image and by using Sobel's Algorithm. In the second step, characteristics of the license plate are used for directly defining a region of the license plate on the image, and boundaries of the license plate are further found out. By using both the steps, the license plate may be located under various environments. In the thesis “On the study of automatic traffic surveillance system”, which is edited by Yu, at the Graduate School of Electrical Engineering from Yuan Ze University, it is indicated that a luminance contrast between characters on the license plate and background on the image. Morphological operations are used for finding regions fitting contrast characteristics of the license plate, and erroneous blocks are filtered off according to geometric properties of the license plate. Images under various environments are also used for robustness of the proposed monitor system.
Some technologies perform the license plate locating according to color characteristics. For example, according to “A study of locating vehicle license plate based on color feature and mathematical morphology”, which is edited by W. G. Zhu, G. J. Hou and X. Jia, and published in Proc. IEEE International Conference on Signal Processing, vol. 1, pp. 748-751, on 2002, the location of the license plate image is determined according to specific colors on the license plate with the aid of morphological operations. The proposed method is appropriate for primary colors indicated by red, green, and blue. In the proposed method, images of a moving vehicle are dynamically fetched, and a vehicle image on the fetched images is found according to differences between the fetched images, so as to reduce calculations and to achieve real-time calculations. In “License Plate Detection System in Rainy Days”, which is edited by Yoshimori S., Mitsukura Y., Fukumi M., Akamatsu N., Khosal R., and is published in IEEE International Symposium on Computational Intelligence in Robotics and Automatic, vol 2, pp. 972-976, on July 2003, a license plate automatic recognition system used under complicated environments is proposed, and the license plate image is located with the aid of Fuzzy Theory, color transformation, and color edge detection, in considerations of characteristics including edges and colors of the license plate. About the color transformation, HSI color transformation is used, where H indicates hue, S indicates saturation, and I indicates intensity of luminance. Since hues of a same color on a same image cannot be affected by luminance, and can be immune from shadows, so that HSI color transformation is appropriate for outdoor environments, and for license plate image recognition as well. In “An adaptive approach to vehicle license plate localization”, which is edited by Guanozhi Cao, Jiaqian Chen, and Jingping Jiang, and is published in IEEE Conference of Industrial Electronics Society, vol. 2, pp. 1786-1791, on 2003, a critical value acquiring robustness is determined from various environments with the aid of the real coded genetic algorithm (RGA), and the critical value is used for searching regions acquiring colors similar with the license plate. In “A Threshold Selection Method from Gray-Level Histogram”, which is edited by N. Otsu, and is published in IEEE Trans. On System, Man and Cybernetics, vol. 9, pp. 62-66, on 1979, an adjustable critical value is also determined by using an algorithm similar with RGA, and is used for defining the location of the license plate.